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The research aimed to explore the potential of advanced machine learning (ML) algorithms in clinical and biomedical research. The significance of frameworks like generative adversarial networks (GANs), autoencoders, and autoregressive models in tackling the issues of representation learning and the quality of generated content is highlighted. This paper also presents a proposed system architecture for integrating generative artificial intelligence (GenAI) into healthcare processes. This architecture encompasses components for data ingestion, preprocessing, model training, image enhancement, diagnostic analysis, and user interfaces for healthcare providers and patients, utilizing advanced artificial intelligence (AI) models. The paper underscores the necessity of robust data governance frameworks, ethical guidelines, and secure infrastructures to mitigate the associated risks. By fostering collaborative AI-human systems and continuously assessing ethical implications, the healthcare industry can fully exploit GenAI's potential to improve patient outcomes and operational efficiency.